Prolytics OS is an advanced analytics and AI-powered intelligence tool designed to solve a fragmented data problem for modern software teams. In a typical tech stack, product behavior data lives in PostHog, revenue tracking happens in Stripe, and application errors are monitored in Sentry. When a core metric shifts unexpectedly, teams traditionally waste valuable time manually cross-referencing and connecting data across all three platforms.

Prolytics OS automates this cross-functional investigation process entirely. The platform features an automated Health system that flags key metrics requiring your immediate attention. Its powerful Engine investigates the underlying root causes by fetching numbers, testing correlations, and digging into ambiguous changes with supporting evidence attached. Finally, built-in Watches monitor your fixes to confirm whether your deployed solutions successfully resolved the issue.

Built with enterprise security and data privacy in mind, Prolytics OS operates strictly in a read-only mode and guarantees that your proprietary business data is never used for model training.

Pros of Prolytics OS

Automatically bridges data silos between PostHog, Stripe, and Sentry
Health monitoring proactively flags metrics that need attention
Engine feature handles deep investigations with attached evidence and correlation testing
Watches confirm the effectiveness of your fixes over time
Prioritizes data security with read-only access and no training on user data

Cons of Prolytics OS

Requires integration specifically with PostHog, Stripe, and Sentry
Launch Access package comes with a notable upfront cost ($499)
Credit-based system limits resource-heavy operations depending on usage

Frequently Asked Questions

What tools does Prolytics OS connect?

Prolytics OS connects data from PostHog (product behavior), Stripe (revenue), and Sentry (errors) to give you a unified view of your business metrics.

How does Prolytics OS investigate metric changes?

Its Engine investigates changes by fetching numbers, testing correlations, digging into unclear changes, and providing attached evidence to explain why a metric moved.